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Research Article | Open Access

Evaluation of modified adaptive k-means segmentation algorithm

Institute of Neural Information Processing, Ulm University, 89081Ulm, Germany;
Addis Ababa Science and Technology University, Addis Ababa, 120611, Ethiopia.
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Abstract

Segmentation is the act of partitioning an image into different regions by creating boundaries between regions. k-means image segmentation is the simplest prevalent approach. However, the segmentation quality is contingent on the initial parameters (the cluster centers and their number). In this paper, a convolution-based modified adaptive k-means (MAKM) approach is proposed and evaluated using images collected from different sources (MATLAB, Berkeley image database, VOC2012, BGH, MIAS, and MRI).The evaluation shows that the proposed algorithm is superior to k-means++, fuzzy c-means, histogram-based k-means, and subtractive k-means algorithms in terms of image segmentation quality ( Q-value), computational cost, and RMSE. The proposed algorithm was also compared to state-of-the-art learning-based methods in terms of IoU and MIoU; it achieved a higher MIoU value.

Keywords

clusteringmodified adaptive k-means (MAKM)segmentationQ-value

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Computational Visual Media
Volume 5 Issue 4,
December 2019
Pages 347-361
DOI: 10.1007/s41095-019-0151-2
Cite this article:
Debelee TG, Schwenker F, Rahimeto S, et al. Evaluation of modified adaptive k-means segmentation algorithm. Computational Visual Media, 2019, 5(4): 347-361. https://doi.org/10.1007/s41095-019-0151-2

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Revised: 01 April 2019
Accepted: 29 June 2019
Published: 24 July 2019
© The author(s) 2019

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